A convolutional neural network-based multiobject detection and tracking algorithm can be applied to vehicle detection and traffic flow statistics, thus enabling smart transportation. Aiming at the problems of the high computational complexity of multiobject detection and tracking algorithms, a large number of model parameters, and difficulty in achieving high throughput with a low power consumption in edge devices, we design and implement a low-power, low-latency, high-precision, and configurable vehicle detector based on a field programmable gate array (FPGA) with YOLOv3 (You-Only-Look-Once-version3), YOLOv3-tiny CNNs (Convolutional Neural Networks), and the Deepsort algorithm. First, we use a dynamic threshold structured pruning method based on a scaling factor to significantly compress the detection model size on the premise that the accuracy does not decrease. Second, a dynamic 16-bit fixed-point quantization algorithm is used to quantify the network parameters to reduce the memory occupation of the network model. Furthermore, we generate a reidentification (RE-ID) dataset from the UA-DETRAC dataset and train the appearance feature extraction network on the Deepsort algorithm to improve the vehicles’ tracking performance. Finally, we implement hardware optimization techniques such as memory interlayer multiplexing, parameter rearrangement, ping-pong buffering, multichannel transfer, pipelining, Im2col+GEMM, and Winograd algorithms to improve resource utilization and computational efficiency. The experimental results demonstrate that the compressed YOLOv3 and YOLOv3-tiny network models decrease in size by 85.7% and 98.2%, respectively. The dual-module parallel acceleration meets the demand of the 6-way parallel video stream vehicle detection with the peak throughput at 168.72 fps.

Graph Convolutional Networks (GCNs) have become a key model for representation learning on graph structured data. The introduction of residual connections further breaks the depth limitation of traditional GCNs, giving rise to Deep Graph Convolutional Networks (DeepGCNs). We observe that in DeepGCNs, as network depth increases, residual feature values progressively accumulate and grow in magnitude, which significantly reduces the model’s sensitivity to variations in intermediate layer features. Consequently, even when strong feature interventions such as aggregation skipping or mean reuse are applied during inference, the residual features of the optimized model still preserve high similarity to those of the original baseline. Based on this observation, this paper proposes a redundancy aware inference algorithm DGCN for DeepGCNs and further designs a dedicated hardware accelerator DeGCN that works in coordination with it. Unlike existing GCNs accelerators that mainly focus on intra layer sparsity and storage optimization, DeGCN explicitly identifies aggregation skipping and mean reuse behaviors and dynamically organizes differentiated data paths and scheduling strategies, thereby avoiding redundant computations in intermediate layers. For the execution characteristics of the initial layers, where features are sparse and memory access is the performance bottleneck, DeGCN incorporates the CBSR adaptive sparse feature storage format and a multi encoding support mechanism to accommodate input features with different sparsity levels, thereby reducing feature access and decoding overhead. In addition, through cross layer scheduling and execution restructuring mechanisms, DeGCN enables coordinated advancement of multi level computations at the architectural level, thereby maximizing execution overlap and improving overall throughput. Compared with HS-GCN, the strongest evaluated prior accelerator, DeGCN achieves a 4.16 × speedup and 5.12 × higher energy efficiency.